Continuous learning of simulation trained deep neural network model

ABSTRACT

The present invention provides a system and method of side-stepping the need to retrain neural network model after initially trained using a simulator by comparing real-world data to data predicted by the simulator for the same inputs, and developing a mapping correlation that adjusts real world data toward the simulation data. Thus, the decision logic developed in the simulation-trained model is preserved and continues to operate in an altered reality. A threshold metric of similarity can be initially provided into the mapping algorithm, which automatically adjusts real world data to adjusted data corresponding to the simulation data for operating the neural network model when the metric of similarity between the real world data and the simulation data exceeds the threshold metric. Updated learning can continue as desired, working in the background as conditions are monitored.

CROSS REFERENCE TO RELATED APPLICATIONS

Not applicable.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO APPENDIX

Not applicable.

BACKGROUND OF THE INVENTION Field of the Invention

The disclosure generally relates to the neural network model trainingand operation using a simulator and adjustment factors for thesimulator. More particularly, the disclosure relates to neural networkmodel training and operation for floating production platforms, vessels,and other floating systems.

Description of the Related Art

In producing hydrocarbons from subsea formations, floating productionplatforms, vessels, and other floating production systems are typicallymoored or otherwise maintained in position during operations. Themooring lines periodically need repair or replacement. Because manymooring lines are typically used, a deteriorated condition or even afailure of a mooring line may not be immediately noticeable to operatorsunder normal conditions. However, extreme conditions may test theintegrity of the mooring line system. If the remaining mooring lines areoverstressed under the extreme conditions, then the entire floatingproduction system may be compromised or fail.

It is desirable for predictive modelling to predict the condition of themooring line system based on real world movement of the floatingproduction systems compared to expected movement. A neural network modelcan provide a robust system that can learn in time and adjust forconditions for more accurate responses. Neural network models (NN model)are systems of interconnected neurons that correlate known inputs toknown outputs in order to generate a complex weighted high order,non-linear polynomial to predict outputs with inputs. Typically, theinputs and outputs are derived from a functioning system, thus requiringconsiderable time before a sufficient set of data is accumulated fortraining the system, and also not be available during that real timefunctioning period.

An alternative is to train the NN model with a simulation of the systemoperation. The simulated input/output greatly speeds up the learningprocess and makes a neural network model available from the beginning ofoperations. However, the initial simulation training necessitatesverifying the simulation accuracy in parallel to the NN model operatingunder real world conditions. The verification typically means generatinga new set of inputs and outputs regarding the simulation accuracyfollowed by retraining the NN model, and then uploading the new NNmodel, adding costs and time to the NN model functioning as intended.

While the NN model provides opportunities of operating improvements offloating production systems, a better system and method is needed totrain the NN model to operate in the given environment and in otherapplications.

SUMMARY OF THE INVENTION

The present invention provides a system and method of side-stepping theneed to retrain neural network model after initially trained using asimulator by comparing real-world data to data predicted by thesimulator for the same inputs, and developing a mapping correlation thatadjusts real world data toward the simulation data. Thus, the decisionlogic developed in the simulation trained NN model is preserved andcontinues to operate in what can be described as an altered reality. Athreshold metric of similarity, such as a percentage of correlation, canbe initially provided into the mapping algorithm, which automaticallyadjusts real world data to adjusted data corresponding to the simulationdata for operating the neural network model when the metric ofsimilarity between the real world data and the simulation data exceedsthe threshold metric. Updated learning can continue as desired, workingin the background as system conditions are monitored, such as for afloating production system. When the real data is within the thresholdmetric, the real data can be provided to the NN model as operationaldata. When the real world data exceeds the threshold metric from thesimulation data, the system can begin to adjust inputs to the NN modelby adjusting the real world data to adjusted data corresponding to thesimulation data. The adjusted data can be provided to the NN model asoperational data. Updated learning can continue as desired, working inthe background as conditions are monitored.

The disclosure provides a method of operating a neural network model,comprising: providing simulation data to the neural network model, thesimulation data including at least one simulated value of a variable andat least one simulation result corresponding to the at least onesimulated value of the variable; providing decision logic to the neuralnetwork model to process the simulation data for a neural network modeloutput; obtaining real world data including at least one real worldvalue of the variable and at least one real world result correspondingto the at least one real world value of the variable; comparing the realworld data with the simulation data to determine a metric of similaritybetween the real world data and the simulation data; providing the realworld data as operational data for the neural network model if themetric of similarity is simulation data greater than a threshold metricto produce an output corresponding to an output with the simulation datafrom the neural network model; adjusting the real world data tocorrespond with the simulation data and providing the adjusted data asthe operational data for the neural network model if the metric is notgreater than the threshold metric; and operating the neural networkmodel with the adjusted data that corresponds at least within the rangeof the simulation data to produce an output with the adjusted datacorresponding to an output with the simulation data from the neuralnetwork model.

The disclosure also provides a system comprising: a central processingunit; a non-transitory computer readable memory including processorexecutable program instructions that, when executed cause the centralprocessing unit to perform operations comprising: storing simulationdata in memory for a neural network model, the simulation data includingat least one simulated value of a variable and at least one simulationresult corresponding to the at least one simulated value of thevariable; storing decision logic simulation data in the memory for theneural network model to process the simulation data; storing real worlddata in the memory including at least one real world value of thevariable and at least one real world result corresponding to the atleast one real world value of the variable; comparing the real worlddata with the simulation data to determine a metric of similaritybetween the real world data and the simulation data; providing the realworld data as operational data for the neural network model if themetric of similarity is greater than a threshold metric simulation datato produce an output corresponding to an output with the simulation datafrom the neural network model; adjusting the real world data tocorrespond with the simulation data and providing the adjusted data asthe operational data for the neural network model if the metric is notgreater than the threshold metric; and operating the neural networkmodel with the adjusted data that corresponds at least within the rangeof the simulation data to produce an output with the adjusted datacorresponding to an output with the simulation data from the neuralnetwork model.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic top view of an example of a floating system withmooring lines and a monitoring system.

FIG. 2 is a schematic flow chart of an example of an embodiment of theinvention.

FIG. 3 is a block diagram that illustrates a computer system 700 uponwhich an embodiment of the invention may be implemented.

DETAILED DESCRIPTION

The Figures described above and the written description of specificstructures and functions below are not presented to limit the scope ofwhat Applicant has invented or the scope of the appended claims. Rather,the Figures and written description are provided to teach any personskilled in the art to make and use the inventions for which patentprotection is sought. Those skilled in the art will appreciate that notall features of a commercial embodiment of the inventions are describedor shown for the sake of clarity and understanding. Persons of skill inthis art will also appreciate that the development of an actualcommercial embodiment incorporating aspects of the present disclosurewill require numerous implementation-specific decisions to achieve thedeveloper's ultimate goal for the commercial embodiment. Suchimplementation-specific decisions may include, and likely are notlimited to, compliance with system-related, business-related,government-related, and other constraints, which may vary by specificimplementation or location, or with time. While a developer's effortsmight be complex and time-consuming in an absolute sense, such effortswould be, nevertheless, a routine undertaking for those of ordinaryskill in this art having benefit of this disclosure. It must beunderstood that the inventions disclosed and taught herein aresusceptible to numerous and various modifications and alternative forms.The use of a singular term, such as, but not limited to, “a,” is notintended as limiting of the number of items. Further, the variousmethods and embodiments of the system can be included in combinationwith each other to produce variations of the disclosed methods andembodiments. Discussion of singular elements can include plural elementsand vice-versa. References to at least one item may include one or moreitems. Also, various aspects of the embodiments could be used inconjunction with each other to accomplish the understood goals of thedisclosure. Unless the context requires otherwise, the term “comprise”or variations such as “comprises” or “comprising,” should be understoodto imply the inclusion of at least the stated element or step or groupof elements or steps or equivalents thereof, and not the exclusion of agreater numerical quantity or any other element or step or group ofelements or steps or equivalents thereof. The device or system may beused in a number of directions and orientations. The terms “top”, “up”,“upward”, “bottom”, “down”, “downwardly”, and like directional terms areused to indicate the direction relative to the figures and theirillustrated orientation and are not absolute in commercial use but canvary as the assembly varies its orientation. The order of steps canoccur in a variety of sequences unless otherwise specifically limited.The various steps described herein can be combined with other steps,interlineated with the stated steps, and/or split into multiple steps.Similarly, elements have been described functionally and can be embodiedas separate components or can be combined into components havingmultiple functions. Some elements are nominated by a device name forsimplicity and would be understood to include a system of relatedcomponents that are known to those with ordinary skill in the art andmay not be specifically described. Various examples are provided in thedescription and figures that perform various functions and arenon-limiting in shape, size, description, but serve as illustrativestructures that can be varied as would be known to one with ordinaryskill in the art given the teachings contained herein. As such, the useof the term “exemplary” is the adjective form of the noun “example” andlikewise refers to an illustrative structure, and not necessarily apreferred embodiment.

The present invention provides a system and method of side-stepping theneed to retrain neural network model after initially trained using asimulator by comparing real-world data to data predicted by thesimulator for the same inputs, and developing a mapping correlation thatadjusts real world data toward the simulation data. Thus, the decisionlogic developed in the simulation-trained model is preserved andcontinues to operate in an altered reality. A threshold metric ofsimilarity can be initially provided into the mapping algorithm, whichautomatically adjusts real world data to adjusted data corresponding tothe simulation data for operating the neural network model when themetric of similarity between the real world data and the simulation dataexceeds the threshold metric. Updated learning can continue as desired,working in the background as conditions are monitored.

FIG. 1 is a schematic top view of an example of a floating system withmooring lines and a monitoring system. A floating system 2 can include,for example and without limitation, a floating production platform,FPSO, drill ship, spar, tension leg platform, semi-submersible ships andrigs, and others. The floating system 2 generally has a hull 4 of someshape from which mooring lines 6 can be coupled generally to the seabed(not shown). Monitoring equipment 8, such as sensors or othermeasurement devices, can be coupled to the mooring lines to monitor oneor more conditions of the mooring line. Output from the monitoringequipment 8 can be communicated to the computer system 700. The computersystem can include software and/or hardware with components and such asdescribed in FIG. 3 below for performing a neural network model 10. Theneural network model can be at least initially trained by a simulator 12using simulation data.

FIG. 2 is a schematic flow chart of an example of an embodiment of theinvention. In step 100, simulation data is used to train the neuralnetwork model (NN model). The simulation data is illustrated in the formof one or more data sets (x, y(x)), where a selected variable x producesa simulation result y as a function of x for a given simulated value ofx. The NN model is trained and produces an output z.

The NN model can be placed into service and real world (RW) datagenerated by the system can be sent to the NN model. In step 200, the RWdata is analyzed to determine if an adjustment factor is needed to therelationship between RW data to maintain the result z produced by the NNmodel when still using the simulation data (or an equivalent to thesimulation data). A threshold metric of similarity can be initiallyprovided into a mapping algorithm, which automatically adjusts realworld data to adjusted data corresponding to the simulation data foroperating the neural network model when the metric of similarity betweenthe real world data and the simulation data exceeds the thresholdmetric. The adjustment is made to the relationship between the RW data,so that the NN model does not need to be retrained. The output result zis at least similar using the simulation data (x, y(x)) after applyingthe adjustment to the RW data. Essentially, the relationship (x, y(x))is pre-filtered based on a more realistic input and output correlation,but without the need to make a substantial change in the decision logicprovided to the NN model. Thus, the decision logic used by the NN modelin handling the simulated relationship between x and y(x) in thetraining can continue to be used by the NN model.

More specifically, in step 210, the RW result y′(x) is generated as afunction of x for a given real world value of x. In step 220, the RWresult y′(x) is provided to a decision block that queries whether thesimulation result y(x) is similar to the RW result y′(x) (which canaccount for different values of x and the corresponding results for y).“Similarity” can be mathematically defined, such as within a certainpercentage or within a range of the simulation result y(x), includingbeing equal, or other metrics. The metric of similarity can be comparedwith a threshold metric of similarity. If the metric of similaritysatisfies the criteria of the threshold metric, then the real world datacan be provided as operational data to the NN model in step 250. Noadjustment is needed in the relationship between the RW data (x, y′(x))and the simulation data (x, y(x)), because the RW data is at leastsimilar to the simulation data. Therefore, the RW data (x, y′(x)) can becommunicated to the NN model, and the NN model resulting output z isstill the same or similar to output z from the simulation data in step120.

However, if the result of the decision block in step 220 is that themetric of similarity of the RW result y′(x) and the simulation resulty(x) does not satisfy the criteria of the threshold metric, then theflow path moves to step 230 for adjustments. In step 230, an adjustmentfactor (AF(x)) is calculated, so that the RW result y′(x) has at least asimilar value as the simulation result y(x). The AF(x) can be a linearor nonlinear adjustment. In step 240, the AF(x) is applied to the RWresult y′(x) to yield an adjusted result y*(x) that is at least similarto the simulation result y(x), and the adjusted data set (x, y*(x)) isat least similar to the simulation data set (x, y(x)). In step 250, theadjusted data set (x, y*(x)) that is similar to the simulation data set(x, y(x)) can be provided as operational data to the NN model thatproduces an output z that is at least similar to the output z in step120.

It is known that the basic nature of NN model is noise/error tolerant.In addition, NN model can be a universal approximator that providesnonlinear input-output mapping such as by multilayer feedforwardnetworks, and other techniques. Thus, the similar values produced by theAF for the (x, y*(x)) can be processed by the NN model to yield thesimilar result z.

The flow chart of FIG. 2 can be used for each variable x₁, x₂, . . .x_(n) that simulates a corresponding result y₁, y₂, . . . y_(n), so thatadjusted data sets (x_(n), y*_(n)(x)) can be applied to the NN model.The flow chart can also be applied to results that are dependent onmultiple factors. For example, a result y may be a function of more thanone variable, such as y(x₁, x₂, . . . x_(n)). The AF can be applied tothe RW result y′ that is dependent on multiple variables to yield theadjusted result y* that is at least similar to the simulation result y.

As an example to illustrate the concepts described above, a simulationdata set might be a vessel offset x of 30 meters produces a result y of100 seconds that indicates an intact line and a result y of 120 secondsthat indicates a mooring line failure. The NN model is trained with thesimulation data to take a series of escalating certain actions z, basedon the simulation data ({30 meters, 100 seconds}: intact line and {30meters, 120 seconds}: failure). However, RW data indicates thesimulation model does not match the stiffness of the system in RW. RWdata suggest that a vessel offset x of 30 meters corresponds to a resulty′ of 110 seconds for an intact line and a result y′ of 132 seconds fora broken line. Then the RW y′ of 110 seconds needs to be adjusted to 100seconds for an intact line, and the RW y′ of 132 seconds needs to beadjusted to 120 seconds for a broken line. The adjusted data (x, y*(x)of ({30 meters, 100 seconds}: intact line and {30 meters, 120 seconds}:failure), being the same as the simulation data (x, y), can be providedto the NN model to produce the same actions z. The NN model does notknow (that is, operates independently) of the adjustment factor, and theNN model operates with the adjusted data as it did with the simulationdata such that this logic ({30 meters, 100 seconds}: intact line and {30meters, 120 seconds}: failure) still holds. Thus, the NN model does notneed to be retrained. (In this example, the neural network modelcapabilities in learning and actions are not fully described as would beappreciated by one with ordinary skill in the art, and the decisionlogic and results could be quite complicated. Therefore, the example isonly illustrative of the underlying concepts of the invention andwithout limitation.)

More broadly, an example of this application is for the detection ofdamaged mooring line based on the long drift periods of a floatingoffshore vessel. In this case, the simulated input to the neural networkmodel could be:

-   -   Vessel position or mean vessel offset    -   Total mass of the vessel (draft of the vessel)    -   Long drift periods of the vessel for the corresponding vessel        position and the total mass of the vessel

Let:

-   -   x₁=mean vessel offset in the surge direction    -   x₂=mean vessel offset in the sway direction    -   x₃=total mass of the vessel    -   y₁=vessel's long drift period in the surge direction from        simulation    -   y₂=vessel's long drift period in the sway direction from        simulation

The output variables of the neural network model are damage indicatorsof each mooring line, one indicator for one mooring line. Then:

y ₁ =f(x ₁ ,x ₂ ,x ₃)

y ₂ =f(x ₁ ,x ₂ ,x ₃)

The long drift periods of the vessel, which are a function of vesselposition and total mass, are based on the results of numericalsimulation, and they are used to train a neural network model (NN model)model. Thus, the trained NN model sees everything in the “simulationworld”.

The corresponding RW results can be described as:

-   -   y′₁=vessel's long drift period in the surge direction in the        real world    -   y′₂=vessel's long drift period in the sway direction in the real        world

In the real world, the long drift periods of the vessel at a givenvessel position and for a given total mass of the vessel could bedifferent from the long drift periods of the vessel in the “simulationworld”. If the vessel's long drift periods in the real world were notthe same as those of the “simulation world”, then the follow inequalitywould apply.

y′ ₁(x ₁ ,x ₂ ,x ₃)≠y ₁(x ₁ x ₂ ,x ₃)

y′ ₂(x ₁ ,x ₂ ,x ₃)≠y ₂(x ₁ ,x ₂ ,x ₃)

If the difference is enough, then transfer functions for adjustmentfactors can be established to transfer the vessel's long drift periodsin the real world to those of the “simulation world”. Once transferredto the ‘simulation world’, these adjustment factors can be used toadjust input to the simulation trained NN model, and the simulationtrained NN model can work as is, since the data has been transferredinto the “simulation world” data.

FIG. 3 is a block diagram that illustrates a computer system 700 uponwhich an embodiment of the invention including the neural network modelmay be implemented. According to one embodiment, the techniquesdescribed herein are implemented by one or more special-purposecomputing devices. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

In at least one embodiment, a computer system 700 includes a bus 702 orother communication mechanism for communicating information, and ahardware processor 704 coupled with bus 702 for processing information.Hardware processor 704 may be, for example, a general-purposemicroprocessor. Computer system 700 also includes a main memory 706,such as a random access memory (RAM) or other dynamic storage device,coupled to bus 702 for storing information and instructions to beexecuted by processor 704, and can include one or more steps shown inthe flowchart of FIG. 2. Main memory 706 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 704. Such instructions, whenstored in non-transitory storage media accessible to processor 704,render computer system 700 into a special-purpose machine that iscustomized to perform the operations specified in the instructions.Computer system 700 further includes a read only memory (ROM) 708 orother static storage device coupled to bus 702 for storing staticinformation and instructions for processor 704. A storage device 710,such as a magnetic disk or optical disk, is provided and coupled to bus702 for storing information and instructions. Computer system 700 may becoupled via bus 702 to a display 712, such as a light emitting diode(LED) display, for displaying information to a computer user. An inputdevice 714, including alphanumeric and other keys, is coupled to bus 702for communicating information and command selections to processor 704.Another type of user input device is cursor control 716, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 704 and for controllingcursor movement on display 712. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane.

Computer system 700 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 700 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 700 in response to processor 704 executing one or more sequencesof one or more instructions contained in main memory 706. Suchinstructions may be read into main memory 706 from another storagemedium, such as storage device 710. Execution of the sequences ofinstructions contained in main memory 706 causes processor 704 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 710.Volatile media includes dynamic memory, such as main memory 706. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire, and fiber optics, including thewires that comprise bus 702. Transmission media can also take the formof acoustic or light waves, such as those generated during radio waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 704 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 700 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 702. Bus 702 carries the data tomain memory 706, from which processor 704 retrieves and executes theinstructions. The instructions received by main memory 706 mayoptionally be stored on storage device 710 either before or afterexecution by processor 704.

Computer system 700 also includes a communication interface 718 coupledto bus 702. Communication interface 718 provides a two-way datacommunication coupling to a network link 720 that is connected to alocal network 722. For example, communication interface 718 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 718 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 718sends and receives electrical, electromagnetic, or optical signals thatcarry digital data streams representing various types of information.

Network link 720 typically provides data communication through one ormore networks to other data devices. For example, network link 720 mayprovide a connection through local network 722 to a host computer 724 orto data equipment operated by an Internet Service Provider (ISP) 726.ISP 726 in turn provides data communication services through theworldwide packet data communication network commonly referred to as the“Internet” 728. Local network 722 and Internet 728 both use electrical,electromagnetic, or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 720and through communication interface 718, which carry the digital data toand from computer system 700, are example forms of transmission media.

The data can be real world data communicated to the system 700 fromsensors, monitors, and other devices on conditions and other selectedparameters, as described above. The data can also be simulation datafrom the simulator as described above. The data can be communicatedthrough the host computer 724, the local network 722, or remotelythrough for example an ISP 726 with access to the Internet 728.

Computer system 700 can send messages and receive data, includingprogram code, through the network(s), network link 720, andcommunication interface 718. In the Internet example, a server 730 mighttransmit a requested code for an application program through Internet728, ISP 726, local network 722, and communication interface 718. Thereceived code may be executed by processor 704 as it is received, and/orstored in storage device 710, or other non-volatile storage for laterexecution.

Other and further embodiments utilizing one or more aspects of theinventions described above can be devised without departing from thedisclosed invention as defined in the claims. For example, other systemsbesides floating production systems can benefit from neural networkmodel training and the efficiency of the invention and are includedwithin the scope of the invention and applicability. As other examples,different linear and non-linear adjustment factors can be used, variousmethods of approximating besides multilayer feedforward methods for theneural network model can be employed, and other variations can occurwithin the scope of the claims.

The invention has been described in the context of preferred and otherembodiments, and not every embodiment of the invention has beendescribed. Obvious modifications and alterations to the describedembodiments are available to those of ordinary skill in the art. Thedisclosed and undisclosed embodiments are not intended to limit orrestrict the scope or applicability of the invention conceived of by theApplicant, but rather, in conformity with the patent laws, Applicantintends to protect fully all such modifications and improvements thatcome within the scope, including equivalents, of the following claims.

What is claimed is:
 1. A method of operating a neural network model,comprising: providing simulation data to the neural network model, thesimulation data including at least one simulated value of a variable andat least one simulation result corresponding to the at least onesimulated value of the variable; providing decision logic to the neuralnetwork model to process the simulation data for a neural network modeloutput; obtaining real world data including at least one real worldvalue of the variable and at least one real world result correspondingto the at least one real world value of the variable; comparing the realworld data with the simulation data to determine a metric of similaritybetween the real world data and the simulation data; providing the realworld data as operational data for the neural network model if themetric of similarity is simulation data greater than a threshold metricto produce an output corresponding to an output with the simulation datafrom the neural network model; adjusting the real world data tocorrespond with the simulation data and providing the adjusted data asthe operational data for the neural network model if the metric is notgreater than the threshold metric; and operating the neural networkmodel with the adjusted data that corresponds at least within the rangeof the simulation data to produce an output with the adjusted datacorresponding to an output with the simulation data from the neuralnetwork model.
 2. The method of claim 1, wherein operating the neuralnetwork model comprises operating the neural network for monitoring afloating system.
 3. The method of claim 1, wherein the simulation dataand real world data can apply to mooring lines on a floating system. 4.The method of claim 1, wherein the simulation data include multiplevariables for the simulation result.
 5. The method of claim 1, whereinproviding simulation data to the neural network model comprisesproviding data for a floating system comprising mean floating systemoffset in the surge direction, mean floating system offset in the swaydirection, and total mass of the floating system as variables and thefloating system long drift period in the surge direction and thefloating system long drift period in the sway direction as results. 6.The method of claim 5, wherein obtaining real world data comprisesobtaining the floating system long drift period in the surge directionin the real world and the floating system long drift period in the swaydirection in the real world.
 7. A system comprising: a centralprocessing unit; a non-transitory computer readable memory includingprocessor executable program instructions that, when executed cause thecentral processing unit to perform operations comprising: storingsimulation data in memory for a neural network model, the simulationdata including at least one simulated value of a variable and at leastone simulation result corresponding to the at least one simulated valueof the variable; storing decision logic simulation data in the memoryfor the neural network model to process the simulation data; storingreal world data in the memory including at least one real world value ofthe variable and at least one real world result corresponding to the atleast one real world value of the variable; comparing the real worlddata with the simulation data to determine a metric of similaritybetween the real world data and the simulation data; providing the realworld data as operational data for the neural network model if themetric of similarity is greater than a threshold metric simulation datato produce an output corresponding to an output with the simulation datafrom the neural network model; adjusting the real world data tocorrespond with the simulation data and providing the adjusted data asthe operational data for the neural network model if the metric is notgreater than the threshold metric; and operating the neural networkmodel with the adjusted data that corresponds at least within the rangeof the simulation data to produce an output with the adjusted datacorresponding to an output with the simulation data from the neuralnetwork model.